Reduced state sequence estimation with soft decision outputs

• US 9,294,225 B2
• Filed: 01/30/2015
• Issued: 03/22/2016
• Est. Priority Date: 06/20/2012
• Status: Expired due to Fees

• Current Assignee: Avago Technologies International Sales Pte Limited (Broadcom, Inc.)
• Original Assignee: MagnaCom, Ltd. (Broadcom, Inc.)
• Inventors: Eliaz, Amir, Reuven, Ilan
• Primary Examiner(s): Ghulamali, Qutbuddin
• Application Number:
  US14/609,655
• Publication Number:
  US 20150256293A1
• Time in Patent Office: 417 Days
• Field of Search: 375/341, 375/343, 375/143, 375/152, 375/346
• US Class Current: 1/1
• CPC Class Codes: H04L 1/0054 Maximum-likelihood or sequential decoding, e.g. Viterbi, Fano, ZJ algorithms
• CPC Class Codes: H04L 2025/03414 Multicarrier
• CPC Class Codes: H04L 2025/0342 QAM
• CPC Class Codes: H04L 2025/03522 Frequency domain
• CPC Class Codes: H04L 25/02 Details ; arrangements for supplying electrical power along data transmission lines systems for transmitting signals via power distribution lines H04B3/54
• CPC Class Codes: H04L 25/03038 with a non-recursive structure H04L25/03031 takes precedence
• CPC Class Codes: H04L 25/03159 operating in the frequency domain H04L25/03165, H04L25/03178 take precedence
• CPC Class Codes: H04L 25/03197 methods of calculation involving metrics
• CPC Class Codes: H04L 25/03821 Inter-carrier interference cancellation [ICI]
• CPC Class Codes: H04L 25/497 by correlative coding, e.g. partial response coding or echo modulation coding transmitters and receivers for partial response systems transversal equalizers at the transmitter end H04L25/03343
• CPC Class Codes: H04L 27/2647 Arrangements specific to the receiver only equalisation H04L27/01
• CPC Class Codes: H04L 27/3405 Modifications of the signal space to increase the efficiency of transmission, e.g. reduction of the bit error rate, bandwidth, or average power

Abstract

A receiver may be operable to receive an inter-symbol correlated (ISC) signal, and generate a plurality of soft decisions as to information carried in the ISC signal. The soft decisions may be generated using a reduced-state sequence estimation (RSSE) process. The RSSE process may be such that the number of symbol survivors retained after each iteration of the RSSE process is less than the maximum likelihood state space. The plurality of soft decisions may comprise a plurality of log likelihood ratios (LLRs). Each of the plurality of LLRs may correspond to a respective one of a plurality of subwords of a forward error correction (FEC) codeword.

Claims

1. A system comprising:

signal processing circuitry and sequence estimation circuitry, wherein;

said signal processing circuitry is operable to receive a signal resulting from a transmitted plurality of symbols, each of said plurality of symbols corresponding to a plurality of subwords;

said signal processing circuitry is operable to output samples of said received signal to said sequence estimation circuitry;

said sequence estimation circuitry is operable to process said samples to generate a plurality of vectors, where each of said plurality of vectors represents a hypothesis as to the values of said plurality of symbols; and

said sequence estimation circuitry is operable to generate soft-decisions as to values of said plurality of symbols and/or values of said plurality of subwords based on a count of occurrences of a particular symbol value and/or particular subword value in said plurality of vectors.

2. The system of claim 1, wherein a weight is applied to each one of said occurrences during generation of said count of occurrences.

3. The system of claim 2, wherein said weight applied to said one of said occurrences is based on one or both of:

which one of said plurality of vectors said one of said occurrences is in; and

a metric calculated for said one of said plurality of vectors.

4. The system of claim 2, wherein:

when said one of said plurality of vectors is a first vector having a first metric, said weight applied to said one of said occurrences is a first weight based on said first metric; and

when said one of said plurality of vectors is a second vector having a second metric, said weight applied to said one of said occurrences is a second weight based on said second metric.

5. The system of claim 1, wherein said count of occurrences includes multiple decision delays in said plurality of vectors.

6. The system of claim 5, wherein:

a weight is applied to each one of said occurrences during generation of said count of occurrences; and

said weight is based on which one of said multiple decision delays said one of said occurrences is at.

7. The system of claim 6, wherein:

when said one of said occurrences is at a first one of said multiple decision delays, said weight is a first weight; and

when said one of said occurrences is at a second one of said multiple decision delays, said weight is a second weight.

8. The system of claim 1, wherein said sequence estimation circuitry is operable to process said soft-decisions using a signal-to-noise ratio (SNR)-dependent function.

9. The system of claim 1, wherein one of said soft-decisions corresponding to one of said plurality of symbols and/or one of said plurality of subwords is based on a ratio of (i) said count of occurrences of said particular symbol value and/or particular subword value in said plurality of vectors to (ii) a count of occurrences of one or more other particular symbol values and/or particular subword values in said plurality of vectors.

10. The system of claim 1, wherein each of said plurality of symbols is a quadrature amplitude modulation (QAM) symbol and each of said plurality of subwords is a bit.

11. A method comprising:

performing in a receiver comprising signal processing circuitry and sequence estimation circuitry;

receiving, by said signal processing circuitry, an signal resulting from a transmitted plurality of symbols, each of said plurality of symbols corresponding to a plurality of subwords;

outputting, by said signal processing circuitry, samples of said received signal to said sequence estimation circuitry;

processing, by said sequence estimation circuitry, said samples to generate a plurality of vectors, where each of said plurality of vectors represents a hypothesis as to the values of said plurality of symbols; and

generating, by said sequence estimation circuitry, soft-decisions as to values of said plurality of symbols and/or values of said plurality of subwords based on a count of occurrences of a particular symbol value and/or subword value in said plurality of vectors.

12. The method of claim 11, comprising:

applying, by said sequence estimation circuitry, a weight to each one of said occurrences as part of generating said count of occurrences.

13. The method of claim 12, wherein said weight applied to said one of said occurrences is based on one or both of:

which one of said plurality of vectors said one of said occurrences is in; and

a metric calculated for said one of said plurality of vectors.

14. The method of claim 12, wherein:

when said one of said plurality of vectors is a first vector having a first metric, said weight applied to said one of said occurrences is a first weight based on said first metric; and

when said one of said plurality of vectors is a second vector having a second metric, said weight applied to said one of said occurrences is a second weight based on said second metric.

15. The method of claim 11, wherein said count of occurrences includes multiple decision delays in said plurality of vectors.

16. The method of claim 15, comprising:

applying, by said sequence estimation circuitry, a weight to each one of said occurrences as part of generating of said count of occurrences, wherein said weight is based on which one of said multiple decision delays said one of said occurrences is at.

17. The method of claim 16, wherein:

when said one of said occurrences is at a first one of said multiple decision delays, said weight is a first weight; and

when said one of said occurrences is at a second one of said multiple decision delays, said weight is a second weight.

18. The method of claim 11, comprising processing, by said sequence estimation circuitry, said soft-decisions using a signal-to-noise ratio (SNR)-dependent function.

19. The method of claim 11, wherein one of said soft-decisions corresponding to one of said plurality of symbols and/or one of said plurality of subwords is based on a ratio of (i) said count of occurrences of said particular symbol value and/or subword value in said multiple survivor vectors to (ii) a count of occurrences of one or more other particular symbol values and/or particular subword values in said plurality of vectors.

20. The method of claim 11, wherein each of said plurality of symbols is a quadrature amplitude modulation (QAM) symbol and each of said plurality of subwords is a bit.